1. Description
The present invention relates to a method for processing a satellite image or general image, and more particularly, to a method for automatically performing an image matching process, an image registration and a precise correction process.
2. Background of the Invention
In general, remote sensing data measured from a satellite or aircraft include a great deal of errors and distortions due to an instrument state upon measurement, an atmospheric condition, a moving direction and posture of a platform, a map projection method, and the like. Thus, these errors or distortions should be eliminated or corrected for image restoration. To this end, geometric correction for correcting a difference between a geometric pattern of a collected image and a reference map is generally performed.
This geometric correction is a process of correcting an inherent geometric distortion of the image. The geometric correction is mainly divided into system correction using a result of systematically analyzing causes of the errors and distortions and precision correction using ground control points.
The system correction is a process of analyzing all the causes of the distortions such as an altitude change and shaking of the platform occurring when collecting the image, optical characteristics of a measuring instrument, topographic undulation, earth's rotation, and a map projection method, calculating an inverse transformation algorithm for correcting the distortion of the collected image using the analysis result, and restoring the collected image to its original state using the inverse transformation algorithm. The system correction has an advantage in that all the distortions included in the same transformation algorithm can be easily corrected if all the causes of the distortions of the collected image can be accurately analyzed so that the inverse transformation algorithm can also be accurately calculated. However, there are still disadvantages in that it is not easy to analyze all the causes of the distortions of the collected image and it is also difficult to precisely correct the distortions of the image in case of severe topographic undulation and high image resolution.
The precision correction is a process of analyzing only a distortion degree of the collected image without considering the distortion causes thereof, finding a correction formula which can be used to correlate the collected image with a reference map using the analysis result, and correcting the image distortion using the correction formula. Here, the control point is a coordinate of a specific position with the same shape which has been extracted from the collected image and the reference map so as to match the image with the reference map.
The distortion correction using the control point has advantages in that it can be used even when it is difficult to know or analyze the distortion cause, and the image distortion can be corrected more accurately than the system correction if the control point may be accurately extracted. However, there is a disadvantage in that if the control point is incorrectly extracted, the accurate correction result cannot be obtained. In the past, an operator has manually extracted the control point while viewing the collected image and the reference map with his/her naked eyes. Thus, a correction result may vary greatly according to a degree of skill of the operator who has extracted the control point. Further, there is inconvenience in that the control point should be obtained again every measuring day even though the same area would be measured. Therefore, many researches on methods for automatically extracting the control point have been recently conducted.
The methods for automatically extracting the control point developed heretofore are largely divided into semi-automatic extraction methods and fully automatic extraction methods.
One of the methods for semi-automatically extracting the control point is disclosed in Korean Patent Laid-Open Publication No. 1999-47500 (published on Jul. 5, 1999). According to the method disclosed in the publication, if the operator selects four control points, a system calculates a transformation formula and an inverse transformation formula used for correcting the image using the selected four control points, selects new additional control points, and finally corrects the image. However, since the operator should initially select and input four or more control points, there still remains a problem of the prior art in that the correction result varies according to the degree of skill of the operator, and much time and higher costs are needed for correcting the image.
On the other hand, as the methods of automatically extracting the control points, there are a method of extracting image characteristics and control points using a wavelet scheme, a method of automatically extracting the control points from Landsat satellite images, a method of automatically extracting the control points using a multispectral band, a method of automatically extracting the control points using a generic algorithm, and an automatic geometric correction technique using a DTM (digital terrain model). These various methods of automatically extracting the control points describes only a process of automatically extracting the control point from the image, but they never suggest or teach a process of finding out and overcoming the errors included in the extracted control points. According to the aforementioned methods, therefore, the control point can be automatically extracted, but there is also a problem in that it is difficult to perform the accurate image registration and the precise image correction due to the errors included in the extracted control points.